Combining Deep Learning and Iterative Reconstruction in Dental Cone-Beam Computed Tomography

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http://urn.fi/URN:NBN:fi:hulib-201908283353
Title: Combining Deep Learning and Iterative Reconstruction in Dental Cone-Beam Computed Tomography
Author: Rautio, Siiri
Contributor: University of Helsinki, Faculty of Science
Publisher: Helsingin yliopisto
Date: 2019
Language: eng
URI: http://urn.fi/URN:NBN:fi:hulib-201908283353
http://hdl.handle.net/10138/305052
Thesis level: master's thesis
Degree program: Matematiikan ja tilastotieteen maisteriohjelma
Master's Programme in Mathematics and Statistics
Magisterprogrammet i matematik och statistik
Specialisation: Sovellettu matematiikka
Applied Mathematics
Tillämpad matematik
Discipline: none
Abstract: Improving the quality of medical computed tomography reconstructions is an important research topic nowadays, when low-dose imaging is pursued to minimize the X-ray radiation afflicted on patents. Using lower radiation doses for imaging leads to noisier reconstructions, which then require postprocessing, such as denoising, in order to make the data up to par for diagnostic purposes. Reconstructing the data using iterative algorithms produces higher quality results, but they are computationally costly and not quite powerful enough to be used as such for medical analysis. Recent advances in deep learning have demonstrated the great potential of using convolutional neural networks in various image processing tasks. Performing image denoising with deep neural networks can produce high-quality and virtually noise-free predictions out of images originally corrupted with noise, in a computationally efficient manner. In this thesis, we survey the topics of computed tomography and deep learning for the purpose of applying a state-of-the-art convolutional neural network for denoising dental cone-beam computed tomography reconstruction images. We investigate how the denoising results of a deep neural network are affected if iteratively reconstructed images are used in training the network, as opposed to using traditionally reconstructed images. The results show that if the training data is reconstructed using iterative methods, it notably improves the denoising results of the network. Also, we believe these results can be further improved and extended beyond the case of cone-beam computed tomography and the field of medical imaging.


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